3D Joint Research Articles

Measurement of 3D joint surface morphology and estimation for shear mechanical behavior: A theoretical model and experimental investigation

The majority of rock engineering instabilities are primarily due to shear slips and failures on the joint surfaces, and the measurement of joint surface morphology is crucial for shear strength evaluation. In this study, the high-precision 3D laser scanner was first used to characterize and digitally reconstruct the surface morphology of various joints. The evaluation method of joint surface roughness was modified according to the 2D profile data and shear direction. Then, the effects of normal stress and initial joint roughness coefficient on the shear mechanical behavior were investigated by experimental method. The damage evolution characteristics and failure mechanism of the joint surface are analyzed by acoustic emission monitoring and image binarization calculations. The research results indicate that as the normal stress and initial joint roughness coefficient increase, the peak shear stress and shear displacement, residual strength, and cumulative AE counts of rock joints display an increasing trend. Based on the evolution of mechanical parameters, a new mechanical model of rock joints to predict shear strength and deformation was developed, which was validated by comparing the theoretical prediction curves with test results. A good concordance was achieved between the shear test and theoretical results, indicating that the developed model can effectively predict the shear strength and deformation of joint surfaces under different normal stress and roughness. This research results are expected to provide a valuable guidance for early warning and evaluation of geological disasters.

A leg model based on anatomical landmarks to study 3D joint kinematics of walking in Drosophila melanogaster.

Walking is the most common form of how animals move on land. The model organism Drosophila melanogaster has become increasingly popular for studying how the nervous system controls behavior in general and walking in particular. Despite recent advances in tracking and modeling leg movements of walking Drosophila in 3D, there are still gaps in knowledge about the biomechanics of leg joints due to the tiny size of fruit flies. For instance, the natural alignment of joint rotational axes was largely neglected in previous kinematic analyses. In this study, we therefore present a detailed kinematic leg model in which not only the segment lengths but also the main rotational axes of the joints were derived from anatomical landmarks, namely, the joint condyles. Our model with natural oblique joint axes is able to adapt to the 3D leg postures of straight and forward walking fruit flies with high accuracy. When we compared our model to an orthogonalized version, we observed that our model showed a smaller error as well as differences in the used range of motion (ROM), highlighting the advantages of modeling natural rotational axes alignment for the study of joint kinematics. We further found that the kinematic profiles of front, middle, and hind legs differed in the number of required degrees of freedom as well as their contributions to stepping, time courses of joint angles, and ROM. Our findings provide deeper insights into the joint kinematics of walking in Drosophila, and, additionally, will help to develop dynamical, musculoskeletal, and neuromechanical simulations.

Movement Quality Assessment of Army Reserve Officers' Training Corps Cadets: A Report of Validity and Normative Data.

Movement quality screening in early-career military populations, like Army Reserve Officers' Training Corps (AROTC) cadets, could decrease the negative impact of musculoskeletal injury observed within the military. Movement quality screening techniques should be valid before being pursued in the field. Normative data describing movement quality of AROTC cadets are also needed. Therefore, the aims of this study were to determine criterion validity of several movement quality assessments and report normative jump-landing kinematics of AROTC cadets. This cross-sectional research was approved by the Institutional Review Board. As part of a larger study, 20 AROTC cadets (21.3 ± 3.4 years; 1.7 ± 0.1 m; 73.8 ± 14.8 kg) had 3-dimensional (3D) and 2-dimensional (2D) kinematic data collected simultaneously while performing a jump-landing task. Variables of interest were 3D hip and knee sagittal, frontal, and transverse joint angles at maximum knee flexion. An experienced rater calculated sagittal and frontal 2D joint angles at maximum knee flexion. Averages of 2D and 3D angles were calculated to describe normative data and for further data analysis. Bivariate correlations between 3D and 2D variables were used to determine criterion validity. Moderate correlations were found between 2D and 3D hip frontal plane angles (P = .05, r =-0.33), 2D and 3D knee sagittal plane angles (P = .04, r = 0.35), and 2D and 3D knee frontal plane angles (P = .03, r = -0.36). Normative values of knee and hip kinematics demonstrated averages of 17.58° of knee adduction, 16.48° of knee external rotation, 11.57° of hip abduction, 10.76° of hip internal rotation, and 103.47° of knee flexion during landings. However, ranges demonstrated that landing patterns vary within AROTC cadets. The normative values of 3D jump-landing kinematic data indicate that movement quality varies greatly within AROTC cadets, and some cadets display potentially injurious movements. Therefore, screening movement quality could be beneficial to determine musculoskeletal injury risk in AROTC cadets. Based on the correlations discovered in this study, we recommend the 2D techniques used in this study be researched further as they may serve as alternatives to expensive, timely 3D techniques that could be better utilized in military environments.

Unified AI-Based Predictive Models for the Ultimate Capacity of Multi-Planar Gapped KK Steel Pipe Joints

The multi-planar steel pipe joints are widely used in communication towers, industrial structures, and offshore platforms. The current design formulas consider this joint as a uniplanar joint and account for the multi-planar effect using empirical correction factors. Recent studies deal with this multi-planar joint as a 3D joint but considering certain loading conditions. Hence, the aim of this research is to develop more general AI-based predictive models for the ultimate capacity of multi-planar gapped KK steel pipe joints, considering both symmetric and asymmetric loading conditions. Three AI techniques were applied to a database of previously published works. These techniques are “Genetic Programming” (GP), “Artificial Neural Network” (ANN), and “Evolutionary Polynomial Regression” (EPR). The prediction accuracies of the developed AI models were compared against two previously published formulas. The results indicated that the developed AI models are much more accurate than the previously published formulas. Also, the results showed that both the ANN and EPR models have almost the same level of accuracy (about 92%), but the EPR model has the advantage of presenting a closed-form equation that could be implemented either manually or using software. Doi: 10.28991/CEJ-SP2024-010-07 Full Text: PDF

Mechanical behavior of rock-like specimens with 3D nonpenetrating and nonpersistent rough joints under uniaxial compression: experimental study

Mechanical behavior of rock-like specimens with 3D nonpenetrating and nonpersistent rough joints under uniaxial compression: experimental study

Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation.

Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint. Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA). Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly. Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.

Evaluating high-resolution computed tomography derived 3-D joint space metrics of the metacarpophalangeal joints between rheumatoid arthritis and age- and sex-matched control participants.

Rheumatoid arthritis (RA) is commonly characterized by joint space narrowing. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides unparalleled in vivo visualization and quantification of joint space in extremity joints commonly affected by RA, such as the 2nd and 3rd metacarpophalangeal joints. However, age, sex, and obesity can also influence joint space narrowing. Thus, this study aimed to determine whether HR-pQCT joint space metrics could distinguish between RA patients and controls, and determine the effects of age, sex and body mass index (BMI) on these joint space metrics. HR-pQCT joint space metrics (volume, width, standard deviation of width, maximum/minimum width, and asymmetry) were acquired from RA patients and age-and sex-matched healthy control participants 2nd and 3rd MCP joints. Joint health and functionality were assessed with ultrasound (i.e., effusion and inflammation), hand function tests, and questionnaires. HR-pQCT-derived 3D joint space metrics were not significantly different between RA and control groups (p > 0.05), despite significant differences in inflammation and joint function (p < 0.05). Joint space volume, mean joint space width (JSW), maximum JSW, minimum JSW were larger in males than females (p < 0.05), while maximum JSW decreased with age. No significant association between joint space metrics and BMI were found. HR-pQCT did not detect group level differences between RA and age-and sex-matched controls. Further research is necessary to determine whether this is due to a true lack of group level differences due to well-controlled RA, or the inability of HR-pQCT to detect a difference.

Markerless 3D kinematics and force estimation in cheetahs

The complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah (Acinonyx jubatus) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs, where it is essential that the methods used are remote and do not constrain the animal’s motion. In this work, we use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We call this approach kinetic full trajectory estimation (K-FTE). We validate the method on a dataset comprising synchronised video and force plate data. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94% PCK using the nose-to-eye(s) length segment as a threshold), while the estimates produce an average root-mean-square error of 171.3N (≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 17.16% of peak force during stride) for the estimated ground reaction force when compared against the force plate data. While the joint torques cannot be directly validated against ground truth data, as no such data is available for cheetahs, the estimated torques agree with previous studies of quadrupeds in controlled settings. These results will enable deeper insight into the study of animal locomotion in a more natural environment for both biologists and roboticists.

Additively manufactured 3D micro scarf adhesive joints

AbstractHybrid manufacturing enables to overcome additive manufacturing (AM) constraints regarding the maximum feasible part dimension and/or complexity through part separation and subsequent adhesive joining of AM sub-parts. To ensure structural integrity of the joint at a minimum use of substrate volume, the AM inherent freedom of design can be exploited by realizing 3D micro scarf adhesive joints. The performance of this novel adhesive joint design was assessed by conducting optical measurements and static tensile tests using samples fabricated by laser-based powder bed fusion of metals (PBF-LB/M).

3D pose estimation using joint-based calibration in distributed RGB-D camera system

This paper proposes a new approach that acquires camera parameters and generates an integrated 3D joint using an RGB-D camera network distributed in an arbitrary location in space. The proposed technique consists of three steps. In the first step, camera parameters are calculated using partial joints as feature points. The internal and external parameters between cameras are not calculated using a specially manufactured calibration plate. In the second step, a 3D joint set is estimated by integrating the joints obtained from each camera using the calculated camera parameters. At the same time, a 3D volumetric model in the form of a point cloud is reconstructed. The third step consists of a joint correction algorithm. The resultant 3D joint with high reliability is estimated by correcting the position of the joint using the 3D point cloud reconstructed previously. The generated 3D joint can accurately express the shape and movement of 3D human. The estimated 3D joint was compared with the joint measured using a motion capture device to evaluate its performance. The temporal standard deviation between two measurements shows very low value from 1.966 mm to 7.99 mm.

A parallel algorithm for three-dimensional numerical manifold elements generation

Numerical Manifold Method (NMM) has gained widespread application in engineering practice due to its capacity to effectively address both continuous and discontinuous problems in a unified framework. With the advancement of 3D-NMM, there remains a deficiency in ready-made preprocessing tools. Hence, the generation of 3D manifold elements (MEs) is the primary prerequisite for 3D-NMM. In this study, a parallel algorithm to generate the MEs is proposed. Firstly, a novel 3D block identification algorithm is developed to improve the modeling efficiency. Then, 3D joint networks are simulated using the geological statistical results. Thirdly, the mathematical cover system is constructed using uniformly regular hexahedral meshes. Subsequently, the physical domain is cut using the Boolean algorithm to generate the related manifold blocks (MBs) and the Union-Find algorithm is used to determine the relationship of MBs with mathematical patches (MPs) and physical patches (PPs). The proposed program incorporates the OpenMP parallel library for CPU parallelization to enhance its efficiency and the OpenGL library is used to build a real-time visualization program. Finally, the proposed program is validated using several representative examples, and the obtained results demonstrate its efficient capability in generating MEs.

Suspended Mooring Line Static Analysis using Internal XFlow Capabilities

In the present study, different configurations of a mooring line under a static case are analyzed using the CFD software SIMULIA XFLOW 2022X. Native XFlow geometries employed in the simulation of small springs are used to perform simulations of mooring systems, along with 6 DOF joints, and performing discretization depending on the necessities. Fairlead tensions are compared to experimental data of cables employed in the mooring of DeepCWind semisubmersible platform at 1/40 scale, and to computational model using OPASS. Additionally, the location of different points of the suspended chain in the resting catenary shape is compared to the Quasi-Static model.

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

3D hand reconstruction via aggregating intra and inter graphs guided by prior knowledge for hand-object interaction scenario

Recently, 3D hand reconstruction has gained more attention in human–computer cooperation, especially for hand-object interaction scenario. However, it still remains challenge to improve estimation accuracy and ensure physical plausibility from a single RGB image. To overcome the challenge, we propose a 3D hand reconstruction network combining the benefits of model-based and model-free approaches to balance accuracy and physical plausibility for hand-object interaction scenario. Firstly, we present a novel topology-aware MANO pose parameters regression module from 2D joints directly. Moreover, we further carefully design a vertex-joint mutual graph-attention module guided by MANO to jointly refine hand meshes and joints, which model the dependencies of vertex-vertex and joint-joint and capture the correlation of vertex-joint for aggregating intra-graph and inter-graph node features respectively. The experimental results demonstrate that our method achieves state-of-the-art performance on recently benchmark datasets HO3DV2 and Dex-YCB, and outperforms all only model-based approaches or model-free approaches.

Self-Supervised 3D Human Mesh Recovery from a Single Image with Uncertainty-Aware Learning

Despite achieving impressive improvement in accuracy, most existing monocular 3D human mesh reconstruction methods require large-scale 2D/3D ground-truths for supervision, which limits their applications on unlabeled in-the-wild data that is ubiquitous. To alleviate the reliance on 2D/3D ground-truths, we present a self-supervised 3D human pose and shape reconstruction framework that relies only on self-consistency between intermediate representations of images and projected 2D predictions. Specifically, we extract 2D joints and depth maps from monocular images as proxy inputs, which provides complementary clues to infer accurate 3D human meshes. Furthermore, to reduce the impacts from noisy and ambiguous inputs while better concentrate on the high-quality information, we design an uncertainty-aware module to automatically learn the reliability of the inputs at body-joint level based on the consistency between 2D joints and depth map. Experiments on benchmark datasets show that our approach outperforms other state-of-the-art methods at similar supervision levels.

Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser

Recently, diffusion-based methods for monocular 3D human pose estimation have achieved state-of-the-art (SOTA) performance by directly regressing the 3D joint coordinates from the 2D pose sequence. Although some methods decompose the task into bone length and bone direction prediction based on the human anatomical skeleton to explicitly incorporate more human body prior constraints, the performance of these methods is significantly lower than that of the SOTA diffusion-based methods. This can be attributed to the tree structure of the human skeleton. Direct application of the disentangled method could amplify the accumulation of hierarchical errors, propagating through each hierarchy. Meanwhile, the hierarchical information has not been fully explored by the previous methods. To address these problems, a Disentangled Diffusion-based 3D human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose. In our approach: (1) We disentangle the 3d pose and diffuse the bone length and bone direction during the forward process of the diffusion model to effectively model the human pose prior. A disentanglement loss is proposed to supervise diffusion model learning. (2) For the reverse process, we propose Hierarchical Spatial and Temporal Denoiser (HSTDenoiser) to improve the hierarchical modelling of each joint. Our HSTDenoiser comprises two components: the Hierarchical-Related Spatial Transformer (HRST) and the Hierarchical-Related Temporal Transformer (HRTT). HRST exploits joint spatial information and the influence of the parent joint on each joint for spatial modeling, while HRTT utilizes information from both the joint and its hierarchical adjacent joints to explore the hierarchical temporal correlations among joints. Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the SOTA disentangled-based, non-disentangled based, and probabilistic approaches by 10.0%, 2.0%, and 1.3%, respectively.

Integrated geophysical methods to constrain subsurface structures of Tulu Moye-Bora-Berecha axial volcanic complex, main Ethiopia rift: Implications for geothermal resources

The Main Ethiopian Rift (MER) is a well-known continental rift whose axial sector is characterized by the occurrence of regularly spaced silicic caldera complexes and central stratovolcanoes, interspersed with large fields of fissural basalts, small mafic scoria cones and numerous young normal faults and fissures. The Tulu Moye-Bora-Berecha volcanic complex is found in the central portion of the MER and includes the Tulu Moye geothermal prospect area. A combination of gravity and magnetic methods was used to better constrain the subsurface volcanic stratigraphy and tectonic structures. Regional and residual anomaly maps were produced from the gravity data and the magnetic data were corrected to produce anomaly and enhanced maps. A complete Bouguer anomaly contour map was produced after the necessary reduction was applied to the gravity data. Due to the geomagnetic field's dipolar nature and since the study area is in the equatorial region (<15°), the reduced-to-equator (RTE) technique was used to minimize external effects and correct the data as if the body had been laid at the magnetic equator. The anomalous source's depth was calculated using a Euler depth solution and spectral analysis approach. Joint 2D forward models on three profiles were developed using GM-SYS of Oasis Montaj software. From the interpretation of the geophysical results, the following conclusions have been reached: (1) the crystalline basement is more raised around Salen ridge and west of it; (2) the main heat source of the geothermal system appears to be a central region of the studied area near Salen ridge and is estimated to be at 4–5 km depth, (3) under Gnaro obsidian dome, the basaltic and silicic volcanic horizon is thin, and the deep-seated regional fault serves as the main conduit for the passage of hot fluid to the surface and (4) the gravity and magnetic anomaly plots, regional-residual maps and enhanced data plots all indicate towards the existence of a major geological feature-a large caldera-comprising of Tulu Moye, Bora and Berecha volcanic centers.

A 2D video-based assessment is associated with 3D biomechanical contributors to dynamic knee valgus in the coronal plane.

Adolescent athletes involved in sports that involve cutting and landing maneuvers have an increased risk of anterior cruciate ligament (ACL) tears, highlighting the importance of identifying risky movement patterns such as dynamic knee valgus (DKV). Qualitative movement screenings have explored two-dimensional (2D) scoring criteria for DKV, however, there remains limited data on the validity of these screening tools. Determining a 2D scoring criterion for DKV that closely aligns with three-dimensional (3D) biomechanical measures will allow for the identification of poor knee position in adolescent athletes on a broad scale. The purpose of this study was to establish a 2D scoring criterion that corresponds to 3D biomechanical measures of DKV. A total of 41 adolescent female club volleyball athletes performed a three-task movement screen consisting of a single-leg squat (SLS), single-leg drop landing (SLDL), and double-leg vertical jump (DLVJ). A single rater scored 2D videos of each task using four criteria for poor knee position. A motion capture system was used to calculate 3D joint angles, including pelvic obliquity, hip adduction, knee abduction, ankle eversion, and foot progression angle. Receiver operating characteristic curves were created for each 2D scoring criterion to determine cut points for the presence of movement faults, and areas under the curve (AUC) were computed to describe the accuracy of each 2D criterion compared to 3D biomechanical data. 3D measures indicated knee abduction angles between 2.4°-4.6° (SD 4.1°-4.3°) at the time point when the center of the knee joint was most medial during the three tasks. AUCs were between 0.62 and 0.93 across scoring items. The MEDIAL scoring item, defined as the knee joint positioned inside the medial border of the shoe, demonstrated the greatest association to components of DKV, with AUCs ranging from 0.67 to 0.93. The MEDIAL scoring criterion demonstrated the best performance in distinguishing components of DKV, specifically pelvic obliquity, hip adduction, ankle eversion, and foot progression. Along with the previously published scoring definitions for trunk-specific risk factors, the authors suggest that the MEDIAL criterion may be the most indicative of DKV, given an association with 3D biomechanical risk factors.

Tapping into the human spinal locomotor centres with transspinal stimulation

Human locomotion is controlled by spinal neuronal networks of similar properties, function, and organization to those described in animals. Transspinal stimulation affects the spinal locomotor networks and is used to improve standing and walking ability in paralyzed people. However, the function of locomotor centers during transspinal stimulation at different frequencies and intensities is not known. Here, we document the 3D joint kinematics and spatiotemporal gait characteristics during transspinal stimulation at 15, 30, and 50 Hz at sub-threshold and supra-threshold stimulation intensities. We document the temporal structure of gait patterns, dynamic stability of joint movements over stride-to-stride fluctuations, and limb coordination during walking at a self-selected speed in healthy subjects. We found that transspinal stimulation (1) affects the kinematics of the hip, knee, and ankle joints, (2) promotes a more stable coordination at the left ankle, (3) affects interlimb coordination of the thighs, and (4) intralimb coordination between thigh and foot, (5) promotes greater dynamic stability of the hips, (6) increases the persistence of fluctuations in step length variability, and lastly (7) affects mechanical walking stability. These results support that transspinal stimulation is an important neuromodulatory strategy that directly affects gait symmetry and dynamic stability. The conservation of main effects at different frequencies and intensities calls for systematic investigation of stimulation protocols for clinical applications.

New 2D roughness parameters with geometric and physical meanings for rock joints and their correlation with joint roughness coefficient

Determining the joint roughness accurately will better serve the peak shear strength estimation models of rock joints used for stability assessment of rock masses. Considering the defects of the existing quantitative characterization parameters for two-dimensional (2D) joint roughness, especially the lack of explicit geometric and physical meaning, we proposed two new 2D roughness parameters, θ2D and h2D. The former, θ2D, represents the average inclination angle of all potential contact asperities over the entire joint profile, while the latter, h2D, characterizes their average undulation height. Both parameters are closely related to the shear strength of rock joints. Then, the roughness parameters θ2D and h2D of 102 rock joint profiles digitized at 0.5 mm sampling interval were calculated, and a new nonlinear regression equation for the determination of the 2D joint roughness coefficient (JRC) was established by combining the calculated results of the two roughness parameters. It was verified that the proposed equation could give accurate JRC estimation values of the 10 standard profiles of rock joints. Through the comparative analysis of the experimental data collected from earlier studies for the peak shear strength of 73 rock joint samples and corresponding estimated values, the equation was further verified to be applicable and accurate for estimating the JRC values of rock joints. Furthermore, we discussed the effects of shear direction and sampling interval on roughness and further provided another equation that could be applied to estimate the JRC values of joint profiles at the sampling interval of 1.0 mm.